Optimization of honeycomb parameters of sandwich composites for energy and specific energy absorption using particle swarm optimization

Sandwich composites are special class of materials because of peculiar properties such as lightweight, high damping and high energy absorbing capacity etc. These properties make them suitable for their use in the shipbuilding and aerospace industry. Honeycomb structures are commonly used core materials in the sandwich composites. The elastic properties of honeycomb are characterized by foil thickness (FT) and cell size (CS) therefore they have significant effect on the mechanical properties. In this manuscript, low velocity impact analysis has been carried out on couple of aluminium honeycomb sandwich composites using Charpy ASTM E−23 machine and the energy absorbed by the sandwich specimens has been recorded. These experimental results have been validated through the numerical simulations carried out using commercially available software Abaqus. The results are found to be in good agreement. The parametric study also has been carried out to investigate the effect of foil thickness (FT), cell size (CS) and core height (CH) on specific energy absorption. Further, sufficient number of samples have been generated from latin hypercube samples (LHS) design and analyzed using Abaqus. Based on these results, polynomial equations have been generated in order to establish the relation between energy dependent variables (Energy absorption and Specific energy absorption(SEA)) and independent variables (Foil Thickness(FT) and Cell size(CS)). These polynomial equations have been used as fitness functions in optimization. The optimization process has been carried out by particle swarm optimization (PSO) matlab code for energy and specific energy absorption. •Influence of honeycomb parameters on the energy absorption and specific energy absorption .•Increase in FT is recommended to design the honeycomb sandwich structure rather than decrease in CS and CH.•Co-relation between the SEA and honeycomb parameters are presented using PRSM.•Proposed best parameters to get optimum SEA using Particle Swarm Optimization.